Systems and methods for correction of artificial deformation in anatomic modeling

ABSTRACT

Systems and methods are disclosed for correcting for artificial deformations in anatomical modeling. One method includes obtaining an anatomic model; obtaining information indicating a presence of an artificial deformation of the anatomic model; identifying a portion of the anatomic model associated with the artificial deformation; estimating a non-deformed local area corresponding to the portion of the anatomic model; and modifying the portion of the anatomic model associated with the artificial deformation, based on the estimated non-deformed local area.

RELATED APPLICATION(S)

This application is a continuation of U.S. application Ser. No.15/429,026, filed Feb. 9, 2017, which is continuation of U.S.application Ser. No. 14/736,853, filed Jun. 11, 2015, now U.S. Pat. No.9,607,386, which is a continuation of U.S. application Ser. No.14/577,705, filed Dec. 19, 2014, now U.S. Pat. No. 9,081,721, which is acontinuation of U.S. application Ser. No. 14/311,605, filed Jun. 23,2014, now U.S. Pat. No. 8,958,623, and claims priority to U.S.Provisional Application No. 61/985,946 filed Apr. 29, 2014, the entiredisclosures of which are hereby incorporated by reference in theirentirety.

FIELD OF THE INVENTION

Various embodiments of the present disclosure relate generally tomedical modeling and related methods. More specifically, particularembodiments of the present disclosure relate to systems and methods forcorrecting artificial deformation in anatomic modeling.

BACKGROUND

Coronary artery disease may cause the blood vessels providing blood tothe heart to develop lesions, such as a stenosis (abnormal narrowing ofa blood vessel). As a result, blood flow to the heart may be restricted.A patient suffering from coronary artery disease may experience chestpain, referred to as chronic stable angina during physical exertion orunstable angina when the patient is at rest. A more severe manifestationof disease may lead to myocardial infarction, or heart attack.

A desire exists to provide more accurate data relating to coronarylesions, e.g., size, shape, location, functional significance (e.g.,whether the lesion impacts blood flow), etc. Patients suffering fromchest pain and/or exhibiting symptoms of coronary artery disease may besubjected to one or more tests that may provide some indirect evidencerelating to coronary lesions. For example, noninvasive tests may includeelectrocardiograms, biomarker evaluation from blood tests, treadmilltests, echocardiography, single positron emission computed tomography(SPECT), and positron emission tomography (PET). These noninvasivetests, however, typically do not provide a direct assessment of coronarylesions or assess blood flow rates. The noninvasive tests may provideindirect evidence of coronary lesions by looking for changes inelectrical activity of the heart (e.g., using electrocardiography(ECG)), motion of the myocardium (e.g., using stress echocardiography),perfusion of the myocardium (e.g., using PET or SPECT), or metabolicchanges (e.g., using biomarkers).

For example, anatomic data may be obtained noninvasively using coronarycomputed tomographic angiography (CCTA). CCTA may be used for imaging ofpatients with chest pain and involves using computed tomography (CT)technology to image the heart and the coronary arteries following anintravenous infusion of a contrast agent. However, CCTA also cannotprovide direct information on the functional significance of coronarylesions, e.g., whether the lesions affect blood flow. In addition, sinceCCTA is purely a diagnostic test, it can neither be used to predictchanges in coronary blood flow, pressure, or myocardial perfusion underother physiologic states (e.g., exercise), nor can it be used to predictoutcomes of interventions.

Thus, patients may require an invasive test, such as diagnostic cardiaccatheterization, to visualize coronary lesions. Diagnostic cardiaccatheterization may include performing conventional coronary angiography(CCA) to gather anatomic data on coronary lesions by providing a doctorwith an image of the size and shape of the arteries. CCA, however, doesnot provide data for assessing the functional significance of coronarylesions. For example, a doctor may not be able to diagnose whether acoronary lesion is harmful without determining whether the lesion isfunctionally significant. Thus, CCA has led to a procedure referred toas an “oculostenotic reflex”, in which interventional cardiologistsinsert a stent for every lesion found with CCA regardless of whether thelesion is functionally significant. As a result, CCA may lead tounnecessary operations on the patient, which may pose added risks topatients and may result in unnecessary heath care costs for patients.

During diagnostic cardiac catheterization, the functional significanceof a coronary lesion may be assessed invasively by measuring thefractional flow reserve (FFR) of an observed lesion. FFR is defined asthe ratio of the mean blood pressure downstream of a lesion divided bythe mean blood pressure upstream from the lesion, e.g., the aorticpressure, under conditions of increased coronary blood flow, e.g., wheninduced by intravenous administration of adenosine. Blood pressures maybe measured by inserting a pressure wire into the patient. Thus, thedecision to treat a lesion based on the determined FFR may be made afterthe initial cost and risk of diagnostic cardiac catheterization hasalready been incurred.

To reduce the above disadvantages of invasive FFR measurements, methodshave been developed for assessing coronary anatomy, myocardialperfusion, and coronary artery flow noninvasively. Specifically,computational fluid dynamics (CFD) simulations have been successfullyused to predict spatial and temporal variations of flow rate andpressure of blood in arteries, including FFR. Such methods and systemsbenefit cardiologists who diagnose and plan treatments for patients withsuspected coronary artery disease, and predict coronary artery flow andmyocardial perfusion under conditions that cannot be directly measured,e.g., exercise, and to predict outcomes of medical, interventional, andsurgical treatments on coronary artery blood flow and myocardialperfusion.

Such CFD simulations may be improved by accurately modeling bloodvessels, since inaccuracies in blood vessel modeling may translate intounreliable assessments. For example, artifacts from imaging orsurrounding anatomy (e.g., myocardial bridging) may influence modelanatomy because artifacts may appear as deformations where there may beno pathological deformations in an actual vessel. Many types of medicalassessments (e.g., measuring minimal lumen diameter, performing bloodflow simulations, or calculating geometric characteristics of a bloodvessel) may be compromised by inaccuracies in patient-specificanatomical (geometrical) blood vessel models. Therefore, a desire existsto construct patient-specific blood vessel models that may correctand/or account for artificial deformations from imaging when extractinga model from images. Such a form of modeling may improve the accuracy ofmedical assessments.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of thedisclosure.

SUMMARY

According to certain aspects of the present disclosure, systems andmethods are disclosed for correcting for artificial deformations inanatomical modeling. One method includes: obtaining an anatomic model;obtaining information indicating a presence of an artificial deformationof the anatomic model; identifying a portion of the anatomic modelassociated with the artificial deformation; estimating a non-deformedlocal area corresponding to the portion of the anatomic model; andmodifying the portion of the anatomic model associated with theartificial deformation, based on the estimated non-deformed local area.

In accordance with another embodiment, a system for anatomical modelingcomprises: a data storage device storing instructions for correctinganatomical modeling; and a processor configured for: obtaining ananatomic model; obtaining information indicating a presence of anartificial deformation of the anatomic model; identifying a portion ofthe anatomic model associated with the artificial deformation;estimating a non-deformed local area corresponding to the portion of theanatomic model; and modifying the portion of the anatomic modelassociated with the artificial deformation, based on the estimatednon-deformed local area.

In accordance with yet another embodiment, a non-transitory computerreadable medium for use on a computer system containingcomputer-executable programming instructions for correcting anatomicalmodeling is provided. The method includes: obtaining an anatomic model;obtaining information indicating a presence of an artificial deformationof the anatomic model; identifying a portion of the anatomic modelassociated with the artificial deformation; estimating a non-deformedlocal area corresponding to the portion of the anatomic model; andmodifying the portion of the anatomic model associated with theartificial deformation, based on the estimated non-deformed local area.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 is a block diagram of an exemplary system and network forcorrecting anatomic modeling, according to an exemplary embodiment ofthe present disclosure.

FIG. 2 is a block diagram of an exemplary method of correcting forartificial deformations in anatomic modeling, according to an exemplaryembodiment of the present disclosure.

FIG. 3 is a block diagram of an exemplary method of specific embodimentsfor correcting various forms of artificial deformations, according to anexemplary embodiment of the present disclosure.

FIG. 4 is a block diagram of an exemplary method of receivinginformation regarding myocardial bridging, according to an exemplaryembodiment of the present disclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of theinvention, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

An accurate patient-specific anatomical (geometrical) blood vessel modelis useful for many types of medical assessments. For example, measuringminimal lumen diameter, performing blood flow simulations, orcalculating geometric characteristics of a blood vessel may beinfluenced by the accuracy of a blood vessel model. However, variousartifacts of the imaging (if the model is extracted from an image) orsurrounding anatomy may cause the vessel to have the appearance of asignificant deformation when there may be no actual pathology shown bythe vessel. Deformations may be due to image artifacts (e.g.,misregistration, streaking artifacts, stents, pacemaker leads, surgicalclips, windmill artifacts), loss of contrast (e.g., due to contrasttiming error), or artificial constriction associated with tissue (e.g.,myocardial bridging). In this disclosure, undesired (non-significant)deformations, due to, for example, imaging artifacts or surroundinganatomy, may be referred to as, “artificial deformations.” Thus, adesire exists for correcting artificial deformations in anatomicalmodeling such that an accurate medical assessment may be made. Thedisclosure may apply to images obtained from any medical imagingmodality, including CT, MR, ultrasound, IVUS, OCT, etc. Therefore, thepresent disclosure is further directed to a new approach for accountingfor artificial deformations in modeling any anatomic, such as, forexample, blood vessels.

Referring now to the figures, FIG. 1 depicts a block diagram of anexemplary system and network for correcting artificial deformation inblood vessel modeling. Specifically, FIG. 1 depicts a plurality ofphysicians 102 and third party providers 104, any of whom may beconnected to an electronic network 100, such as the Internet, throughone or more computers, servers, and/or handheld mobile devices.Physicians 102 and/or third party providers 104 may create or otherwiseobtain images of one or more patients' cardiac and/or vascular systems.The physicians 102 and/or third party providers 104 may also obtain anycombination of patient-specific information, such as age, medicalhistory, blood pressure, blood viscosity, etc. Physicians 102 and/orthird party providers 104 may transmit the cardiac/vascular imagesand/or patient-specific information to server systems 106 over theelectronic network 100. Server systems 106 may include storage devicesfor storing images and data received from physicians 102 and/or thirdparty providers 104. Server systems 106 may also include processingdevices for processing images and data stored in the storage devices.

FIG. 2 is a block diagram of an exemplary method 200 of correcting forartificial deformation in anatomic modeling (e.g., blood vesselmodeling), according to an exemplary embodiment. In one embodiment, step201 may include receiving a model of anatomy that contains explicit orimplicit local area information. For example, such information mayinclude a three-dimensional model with centerline and/or areainformation. In one embodiment, the model may be received on anelectronic storage device (e.g., hard drive, network drive, randomaccess memory (RAM), etc.). In one embodiment, step 203 may includereceiving information indicating that a portion of the vessel model hasan artificial deformation. For example, such information may includecomputations, user input (e.g., from an operator), algorithm output(s),etc. In some instances, such information may be received, also, on anelectronic storage device. In one embodiment, step 205 may includedetermining a portion of the anatomic model affected by the artificialdeformation. Step 207 may include estimating a local area for anon-deformed anatomy in the region of the area affected by theartificial deformation. In one embodiment, step 209 may includemodifying the anatomic model within the portion affected by theartificial deformation, such that the anatomic model geometry is changedto the estimated local area for a non-deformed anatomy. In someembodiments, steps 205-209 may be performed using a computational device(e.g., a computer, a laptop, a cloud computing service, a tablet, a cellphone, etc., such as, of server systems 106). Step 211 may includeoutputting the modified anatomic model to an electronic storage device.

Further embodiments may include performing simulations using themodified blood vessel model. For example, simulations may take intoaccount specific patient data, imaging data, collective patientpopulation data, etc. Medical assessments or diagnoses may be formedfrom simulations based on the modified blood vessel model output frommethod 200.

FIG. 3 is a block diagram of an exemplary method 300 of specificembodiments for correcting artificial deformations from variousartifacts, according to an exemplary embodiment. In one embodiment, step301 may include receiving a model of a coronary blood vessel thatcontains explicit or implicit local area information. For instance, step301 may include receiving a 3-D model with centerline and/or areainformation. In some cases, exemplary models include 3-D geometricalmodels (e.g., a triangulated surface mesh or tetrahedralized 3-D mesh)or a centerline with a radius (or area) associated with each centerlinepoint. The model may be received on an electronic storage device (e.g.,a hard drive, network derive, RAM, etc. of server systems 106).

Step 303 may include receiving information indicating that a portion ofthe vessel model includes an artificial deformation. As previouslydiscussed, artificial deformations may be due to image artifacts (e.g.,misregistration, streaking artifacts, stents, pacemaker leads, surgicalclips, windmill artifacts, etc.), loss of contrast (e.g., due tocontrast timing error), and/or artificial constriction associated withtissue (e.g., myocardial bridging). The information may thus include anindication that a portion of the imaged vessel may be intersected by amisregistration, a motion artifact, and/or a portion of the myocardium.

In one embodiment, a misregistration may include artifacts caused by aslight offset between, for example, fat and water, such that differentvoxels may appear to indicate the fat and water, respectively, even whenthe fat and water may be represented as the same voxel. Misregistrationmay be detected by any desired means, including a normalizedcross-correlation computation between neighboring slices of a computedtomography (CT) image. A misregistrartion may also be determinedvisually by an operator. In one embodiment, a motion artifact may causean artificial deformation, for instance, bulging in a vessel due toblurring or ghosting from varying phase and amplitude associated withimaging acquisition. A motion artifact may be detected by severaldesired means, including computing a measure of local image blur in a CTand/or magnetic resonance (MR) image. Like misregistrations, motionartifacts may be determined visually by an operator. In one embodiment,a model intersected by a portion of the myocardium may cause an apparentdeformation, such as, for example, a narrowing of a blood vessel. Themyocardium may be detected visually by an operator or automatically, byemploying an image segmentation algorithm. In one embodiment, step 303may including receiving the information on the misregistration, motionartifact, and/or myocardium via an electronic storage device.

In one embodiment, step 305 may include defining an area affected by theartificial deformation. For example, step 305 may include defining anarea affected by an artificial deformation for a portion of the vesselmodel near the intersection with the misregistration artifact. In somecases, the area may be determined by the magnitude of thecross-correlation value, or it may be determined visually by anoperator. Alternately or in addition, step 305 may include defining anarea affected by an artificial deformation for a portion of the vesselmodel near the intersection with the motion artifact. In some scenarios,the area may be determined by the magnitude of the measured image blur.Like a misregistration, a motion artifact may be determined visually byan operator. In yet another alternative or additional embodiment, step305 may include defining an area affected by an artificial deformationfor a portion of the vessel model near the intersection with themyocardium. For example, the area may be determined by finding theregion of intersection between the vessel and the myocardium. Anothermeans of determining the affected area may include finding a portion ofvessel size that narrows near the myocardium and then returns to anexpected vessel size distal to the myocardium. One embodiment forfinding a region of intersection between the vessel and the myocardiummay be found at FIG. 4.

In one embodiment, step 307 may include estimating a local area for anon-deformed vessel. For example, estimating the local area for anon-deformed vessel may include determining the size of the vesseloutside (e.g., either proximal and/or distal to) the affected area. Forexample, the radius for estimating the area may be determined bymeasuring the radius proximal and/or distal to the deformation andcomputing an average radius as the estimate. The radius may also, oralternately, be estimated by using a robust kernel regression of thevessel radii along the centerline to determine an idealized radius inthe affected region. In such a case employing robust kernel regression,the idealized radius may be determined with respect to a conditionalprobability distribution, given the centerline. The radius may also beestimated by referring to a database of similar patients, vessels, andlocations when no deformation occurred. In the presence of a bifurcationwithin the affected area, the estimated radii for both branches of thebifurcation may be set to fit Murray's Law.

In one embodiment, step 309 may include modifying the blood vessel modelwithin the portion affected by the artificial deformation, where themodification may include changing the affected portion of the bloodvessel model to match the estimated local area for a non-deformedvessel. The modification may be performed using several methods. Forexample, one method may include creating a model with a constant radiuscentered on the centerline. The radius may be the radius determined fromstep 307. Another method may include smoothly interpolating the modelradius between the proximal and distal regions of the affected region.Yet another method may include matching the radius with an idealizedradius. In some cases, the idealized radius may be obtained via kernelestimation or via the reference database of similar patients. In oneembodiment, steps 305-309 may be performed using a computational device(e.g., a computer, a laptop, a cloud computing service, a tablet, a cellphone, etc., such as server systems 106). Step 311 may includeoutputting the modified blood vessel model to an electronic storagedevice.

FIG. 4 is a block diagram of an exemplary method 400 of receivinginformation regarding myocardial bridging, according to an exemplaryembodiment. Specifically, method 400 may include finding a region ofintersection between a vessel and a myocardium. In one embodiment, step401 may include an image, e.g., a three-dimensional CT volume. Next,step 403 may include finding myocardial bridging based on the image. Forinstance, step 403 may include distinguishing between fat and themyocardium. While fat may not constrict a vessel, the myocardium orother fibrous tissue may, so differentiating between the myocardium andfat may dictate whether to modify an anatomic model. In one embodiment,step 403 may include analyzing Hounsfield scale (HU) measurements inregions of images surrounding vessels. Fat may have a lower HU than themyocardium. Thus, comparisons of images based on local radiodensity mayindicate that a vessel is surrounded by fat rather than the myocardium(or vice versa). Myocardial bridging may exist when myocardium istouching an artery. Therefore, step 403 may further include determiningwhether a myocardium is in contact with vessel.

If contact between a myocardium and a vessel is detected, step 405 mayinclude determining the extent of the contact. For instance, myocardialbridging may include when myocardial tissue completely surrounds avessel and/or where a circumference of a vessel is partly surrounded bymyocardial tissue, to the extent that the myocardium causes a taperingand/or reduction of cross-sectional area of at least a portion of thevessel. Step 405 may include finding an extent of myocardial bridging byway of tunneling (e.g., amount that vessel dips into muscle of themyocardium) and/or tapering in vessels. Step 407 may further includecomputing a radius and/or local area for a non-bridged region or portionof the vessel. Step 409 may include modifying the blood vessel modelwithin the bridged portion, based on radius and/or local area computedin step 407. For example, modifications may include changing theaffected portion of the blood vessel model to reflect the estimatedlocal area for a non-deformed vessel. Methods described in step 309 mayalso be used for making the modifications in step 409. For example,modifying the blood vessel model in step 409 may also include creating amodel with a constant radius centered on the centerline (e.g., using aradius computed in step 407), interpolating model radius betweenproximal and distal portions of an affected region of a model, matchinga radius to an idealized radius, obtaining a radius via a kernelestimation or via the reference database of similar patients, etc. Inone embodiment, steps 405-409 may be performed using a computationaldevice (e.g., a computer, a laptop, a cloud computing service, a tablet,a cell phone, etc., such as server systems 106). Step 411 may includeoutputting the modified blood vessel model to an electronic storagedevice. In some embodiments, method 400 may be performed based on userinput. In other embodiments, portions of method 400 may be automatedand/or computer-assisted.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

What is claimed is:
 1. A computer-implemented method of correcting anatomical modeling, the method comprising: obtaining a plurality of patient-specific images including a vessel and a portion of myocardial tissue; generating or receiving a vessel model based on the obtained patient-specific images; identifying a portion of the vessel model in proximity to the portion of myocardial tissue; determining an expected vessel size of the identified portion of the vessel model; determining a narrowing of the vessel model by comparing a size of the identified portion of the vessel model to the determined expected vessel size; and determining an extent of myocardial bridging, based on the narrowing of the vessel model.
 2. The method of claim 1, further including: detecting the portion of the myocardial tissue relative to the vessel model by segmenting an image of the plurality of patient-specific images.
 3. The method of claim 1, further including: modifying the vessel model based on the expected vessel size of the vessel model.
 4. The method of claim 1, further including: estimating the expected vessel size by interpolating a radius between radii at multiple regions of the anatomic model, using an idealized radius, using a constant radius, using a kernel estimation, or using a database of patient data.
 5. The method of claim 1, further including: determining a first local radiodensity of a first image of the patient-specific images; determining a second local radiodensity of a second image of the patient-specific images; and detecting contact between the vessel and the myocardial tissue based on a comparison of the first local radiodensity and the second local radiodensity.
 6. The method of claim 3, further including: performing a blood flow simulation using the modified vessel model.
 7. The method of claim 6, further including: providing a medical assessment based on the blood flow simulation.
 8. A system for correcting anatomical modeling, the system comprising: a data storage device storing instructions for correcting anatomical modeling; and a processor configured to execute the instructions to perform a method including: obtaining a plurality of patient-specific images including a vessel and a portion of myocardial tissue; generating or receiving a vessel model based on the obtained patient-specific images; identifying a portion of the vessel model in proximity to the portion of myocardial tissue; determining an expected vessel size of the identified portion of the vessel model; determining a narrowing of the vessel model by comparing a size of the identified portion of the vessel model to the determined expected vessel size; and determining an extent of myocardial bridging, based on the narrowing of the vessel model.
 9. The system of claim 8, wherein the at least one computer system is further configured for: detecting the portion of the myocardial tissue relative to the vessel model by segmenting an image of the plurality of patient-specific images.
 10. The system of claim 8, wherein the at least one computer system is further configured for: modifying the vessel model based on the expected vessel size of the vessel model.
 11. The system of claim 8, wherein the at least one computer system is further configured for: estimating the radius or estimating the local area by interpolating a radius between radii at multiple regions of the anatomic model, using an idealized radius, using a constant radius, using a kernel estimation, or using a database of patient data.
 12. The system of claim 8, wherein the at least one computer system is further configured for: determining a first local radiodensity of a first image of the patient-specific images; determining a second local radiodensity of a second image of the patient-specific images; and detecting contact between the vessel and the myocardial tissue based on a comparison of the first local radiodensity and the second local radiodensity.
 13. The system of claim 10, wherein the at least one computer system is further configured for: performing a blood flow simulation using the modified vessel model.
 14. The system of claim 13, wherein the at least one computer system is further configured for: providing a medical assessment based on the blood flow simulation.
 15. A non-transitory computer readable medium for use on a computer system containing computer-executable programming instructions for performing a method of correcting anatomical modeling, the method comprising: obtaining a plurality of patient-specific images including a vessel and a portion of myocardial tissue; generating or receiving a vessel model based on the obtained patient-specific images; identifying a portion of the vessel model in proximity to the portion of myocardial tissue; determining an expected vessel size of the identified portion of the vessel model; determining a narrowing of the vessel model by comparing a size of the identified portion of the vessel model to the determined expected vessel size; and determining an extent of myocardial bridging, based on the narrowing of the vessel model.
 16. The non-transitory computer readable medium of claim 15, the method further comprising: detecting the portion of the myocardial tissue relative to the vessel model by segmenting an image of the plurality of patient-specific images.
 17. The non-transitory computer readable medium of claim 15, the method further comprising: modifying the vessel model based on the expected vessel size of the vessel model. 